University of California Institute of Transportation Studies

Little is known about demand for EVs in the mass market. In this paper, we exploit a natural experiment that provides variation in large EV subsidies targeted at low- and middle-income households in California. Using transaction-level data, we estimate two important policy parameters using triple differences: the subsidy elasticity of demand for EVs and the rate of subsidy pass-through. Estimates show that demand for EVs amongst low- and middle-income households is price-elastic and pass-through is complete. We use these estimates to calculate the expected subsidy bill required for California to reach its goal of 1.5 million EVsby 2025.

There is growing interest in the use of hydrogen as a transportation fuel but the environmental benefits of using hydrogen depend critically on how it is produced and distributed. Leading alternatives to using fossil natural gas to make hydrogen through the conventional method of steam methane reforming include using electrolyzers to split water into hydrogen and oxygen, and the use of biogas as an alternative feedstock to fossil natural gas. This report examines the latest carbon intensity (CI) estimates for these and various other hydrogen production processes, adding important nuances to the general “colors of hydrogen” scheme that has been used in recent years. CI values for hydrogen production can vary widely both within and across hydrogen production pathways. The lowest CI pathways use biomass or biogas as a feedstock, and solar or wind power. The report also analyses jobs creation from new hydrogen production facilities and shows that these benefits can be significant for large-scale facilities based on either future biomass/biogas-to-hydrogen or solar-hydrogen production technologies. Recommendations include setting stricter goals for the state’s Low Carbon Fuel Standard (LCFS) program to continue to reduce the carbon footprint of California’s transportation fuels.

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs  Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data.  It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets and debt, and policy efforts to meet climate goals.To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/a neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans.The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide.

Most net-zero emissions targets require electrification of the entire light-duty vehicle fleet, and before that the electrification of all new vehicle sales. In this paper, we review literature on demand-side issues related to achieving 100% zero-emissions vehicle sales, focusing on plug-in electric vehicles (PEVs). We discuss potential demand-side challenges to increasing PEV sales and related research gaps, including consumer factors (perceptions, knowledge, and consumer characterises), demand-focused policy (incentives), infrastructure, and energy prices. While global PEV sales have substantially increased in recent years, several challenges remain: some demographic groups are currently underrepresented among PEV buyers (e.g. renters, lower income buyers), some car drivers are resistant to PEVs, incentives are influential but have predominantly benefited higher-income new-car buyers and are being phased out, infrastructure is not sufficiently developed or equally distributed, infrastructure is not user friendly, and some households lack charging access. Some issues we identify may be related to the early stage of the PEV market, though will need to be addressed to reach higher PEV sales and PEV fleet shares. Finally, we outline areas where more research is needed to understand and guide the PEV transition.

Connected and automated vehicles (CAV) have the potential to confer large benefits to California in economic development (job creation) and in improving the operation of its road transportation network. CAV systems are likely to become one of the most important application domains for modern information technology, employing large numbers of highly skilled people in research, development and implementation wherever the companies that are developing these systems find the local environment most hospitable. The CAV systems are expected to produce significant improvements in roadway capacity, traffic flow smoothness, driving comfort and convenience, energy efficiency, pollution reduction and traffic safety.

Many U.S. states are supporting the transition of the heavy-duty vehicle (HDV) sector to zero-emission vehicles (ZEVs), with California leading the way through its policy and regulatory initiatives. Within various HDV fleet segments, California’s drayage fleets face stringent targets, requiring all vehicles newly registered in the Truck Regulation Upload, Compliance, and Reporting System to be ZEVs starting January 2024, and all drayage trucks in operation to be zero-emission by 2035. Understanding fleet operator behavior and perspectives is crucial for achieving these goals; however, it remains a critical knowledge gap. This study investigates the preferences and influencing factors for ZEVs among drayage fleet operators in California. We conducted a stated preference choice experiment survey, developed based on previous qualitative studies and literature reviews. With participation from 71 fleets of various sizes and alternative fuel adoption status, we collected 648 choice observations in a dual response design, consisting of a forced choice between ZEVs and a free choice between ZEVs and status quo alternatives. Multinomial logit model analyses revealed driving range and purchase costs as significant factors for ZEV adoption, with charging facility construction costs also critical in hypothetical choices between ZEVs and status quo alternatives. Fleet or organization size also influenced ZEV choices, with large fleets more sensitive to operating costs and small organizations more sensitive to off-site stations. These findings enhance our understanding in this area and provide valuable insights for policymakers dedicated to facilitating the transition of the HDV sector to zero-emission.

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, we consider how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time-use, telecommuters spent less time on work activity but more time on caring for household members, household chores, eating, socializing and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time-use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

Given that small fleets (defined as those with 20 or fewer vehicles) represent a considerable portion of the heavy-duty vehicle (HDV) sector, understanding their perspectives, along with those of large fleets, on zero-emission vehicles (ZEVs) and related policies is crucial for achieving the U.S. HDV sector’s ZEV transition goals. However, research focusing on small fleets or comparing both segments has been limited. Focusing on California’s drayage sector with stringent ZEV transition targets, this study investigates the awareness and perceptions of small and large fleet operators on ZEV technologies and policies established to promote ZEV adoption. Using a fleet survey, we obtained 71 responses from both small and large fleets. We employed a comprehensive exploratory approach, utilizing descriptive analysis, hypothesis testing, and thematic analysis. Findings reveal that both segments generally rated their ZEV knowledge as close to neutral, with about a third reporting limited awareness of the ZEV policy. Both segments highlighted various adoption barriers, including challenges with infrastructure, costs, and operational compatibility. Business strategies under the ZEV policy differed significantly: small fleets planned to delay or avoid ZEV procurement, with some considering relocation, while large fleets were more proactive, with many already having procured or preparing to procure ZEVs. Both segments voiced concerns about the disproportionate impact on small fleets. The findings enhance our understanding of equity issues in ZEV adoption across fleet segments and offer valuable insights for policymakers committed to a more equitable distribution of the impacts.